Looking for approach for monetary evaluation of AI potential in 2025
It would be irresponsible to try. Several in the past, dating back centuries led to various trends that some call winters. I don't agree they are winters. They are just people returning to reality. Creating intelligence in computers has been progressing forward steadily since Charles Babbage. It has accelerated due to advances in digital electronics, in discrete mathematics, and in brain imaging.

It is possible to predict, with some unavoidable uncertainty, the impact on the profitability of existing products or services five years into the future, provided the AI efforts in the corporation are planned at a high level first.

The main issue is that what we mean when we say the word intelligence is really a collection of abilities. Tesla died with no assets yet his designs are in our appliances and the grids that power them. A billionaire may not be able to draw a sketch of himself worth a penny, and Michelangelo may not have been able to design a way to transmit power to homes and businesses.

There is no one thing called intelligence. It is many things. That's why computers exceeded humans in arithmetic long ago, but there are no combinations of hardware and software that can even come close to writing a physics textbook or even passing an arbitrary high school physics final exam. There is much more not known about intelligence then there is known about it.

Monetary evaluation may be necessary as part of the solution, but the end goal is monetary projection. That means one must evaluate the state and trajectory of something AI experts don't yet understand well. We can guess accurately that, if the direction of an AI department or AI groups within multiple existing departments is well managed, gains can be made. We can evaluate the use of AI in the past within the same business type and use the trend we see to make projections along the same line based on how technology trends usually progress. We can also factor in the emergence of competitive businesses also progressing with their use of AI.

That purchasing, R&amp;D, manufacturing, and other departments are interested in AI, have done some initial play with examples and maybe built out some proof of concept code but are unwilling to quantify future potential is not surprising. It is not any easy financial modelling task. To begin the task, some information must be collected and scrutinized to ensure it is an accurate starting assessment.


What are the kinds of products and services of the business?
What are the critical corporate functions that support those?
What are the key technological, market, and production characteristics of the business that places it in a group of like businesses?
What currently developed and known-to-be-effective AI technologies most likely produce profit enhancements for this business?
What past technology advancements are most like those current ones?
Quantitatively, what were the financial outcomes of the leveraging of those past technologies over time?
How is competitive leveraging of the same technologies by competitive businesses likely to draw business and divide profit margins?


Once this information is gathered and its accuracy is ensured, a financial model can be constructed and implemented in software and projections of various scenarios can be made. It is a development project in itself. Many businesses are bypassing this project and just assuming that the current AI craze must have a high ROI because large corporations have made the choice down that road. This question exhibits a more prudent course of action.

In addition to the departments listed, there are a few more to consider.


Marketing
Finance
Strategic planning (at the board and senior management level)
Accounts receivable